Programmed user-defined functions using Python packages like Pandas, NumPy, JSON, and Itertools for the optimized ingestion flow with a data orchestration tool called Prefect. Performed Python Unit testing of developed code snippets using pytest and mock. Maintained data Pipelines up to 99.8% while ingesting streaming and transactional data across various data sources using Python and Prefect. Ingested data from disparate data sources using a combination of SQL, and Google Analytics API using Python to create data views to be used in BI tools like Tableau and PowerBI. Created multiple code snippets of DBT for Data Transformation, creating transformed tables and views in Big Query Production datasets. Performing joins, group by, and other aggregate operations as per Business rules on GCP Big query. Data Modelling of the raw ingested data using SQL, Python, and DBT tools while increasing the quality and consistency of the business applications. Migration from atomic layer to DBT, while automating the ETL process across billions of rows of data. Built an ETL that ingests historical and transactional data from a Web API with about 12,000 daily actives users. Analyzed data of developers in the SecAPI where the issues are about a million records in a day and ingested this data in Big Query through the Google Storage Buckets while using Python CICD. Worked with event-based Google Cloud functions which trigger the ingestion pipelines when there is new data in the storage buckets. Used Kanban framework to implement agile and DevOps software development which requires real time communication and full transparency of work.